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Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. To open the black-box of these deep learning models, post-hoc instance-level explanation methods have been proposed to understand GNN predictions. These methods seek to discover substructures that explain the prediction behavior of a trained GNN. In this paper, we show analytically that for a large class of explanation tasks, conventional approaches, which are based on the principle of g
Artificial Neural Networks can be described as the biologically inspired simulations that are performed on a computer to do a specific set of tasks. Read More
Abstract page for arXiv paper 2410.00580: Deep activity propagation via weight initialization in spiking neural networks
War of the networks From supermemo.guru This article by Dr Piotr Wozniak is part of SuperMemo Guru series on memory, learning, creativity, and problem solving. Definition War of the networks is a major signaling conflict within a neural control system. In the long run, the conflict is resolved by a set of mechanisms of which neuronal death is the most drastic. Rationale I introduced the dramatic term, "war of the networks", to illustrate the essential neural root cause of a large number of human mental and
Crash Introduction to Artificial Neural Networks by Ivan Galkin, U. MASS Lowell (Materials for UML 91.531 Data Mining course) 1. Neurobiological Background Neural Doctrine The nervous system of living organisms is a structure consisting of many elements working in parallel and in connection with one another 1836 Discovery of the neural cell of the brain, the neuron The Structure of Neuron Source of the diagram: NIBS Pte Ltd. This is a result worth of the Nobel Prize [1906]. The neuron is a many-inputs / one
End-to-end learning of semantic role labeling using recurrent neural networks Zhou & Xu International joint conference on Natural Language Processing, 2015 Collobert’s 2011 paper that we looked at yesterday represented a turning point in NLP in which they achieved state of the art performance on part-of-speech tagging (POS), chunking, and named entity recognition (NER) using
# NeurIPS Poster Graph Clustering with Graph Neural Networks Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. Graph clustering has the same overall goal as node pooling in GNNs—does this mean that GNN pooling methods do a good job at clustering graphs? Surprisingly, the answer is no
Skip to content Search TNOC Magazine About What is TNOC? Partner + Contribute Non-discrimination and Gender Policy Privacy Policy (GDPR) Terms + Conditions Arts TNOC Festivals BERLIN FESTIVAL 2024 Paris SUMMIT 2019 Previous Festivals Projects ESSAY Neural Networks—A New Model for “The Kind of Problem a City Is” by Mathieu Hélie 29 April 2018 Art, Science, Action: Green Cities Re-imagined Mathieu Hélie Montréal Mathieu Hélie is a software developer on weekdays and a complexity scientist and
# Recurrent Neural Networks Tutorial, Part 3 This the third part of the Recurrent Neural Network Tutorial . In the previous part of the tutorial we implemented a RNN from scratch, but didn’t go into detail on how Backpropagation Through Time (BPTT) algorithms calculates the gradients. In this part we’ll give a brief overview of BPTT and explain how it differs from traditional backpropagation. We will then try to understand the vanishing gradient problem, which has led to the development of LSTMs and